Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

spatial data (Gangnam in Seoul).

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1^st, 2020 to December 15^th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

Replication data for the poverty rates example in Chapter 4 of Spatial Analysis for the Social Sciences.

Replication data for the turnout example in Chapter 5 of Spatial Analysis for the Social Sciences.

Open-data from LAPD's civic portal were scrubbed, moved into GeoDa software, and given iterations of exploratory spatial data analysis. July 2021This then is the author's replication data by filenames:UrbanAmUnderArrestLA-Arrests2020AugustPSAI.xlsx,UrbanAmUnderArrestByrneLAReportingDistrictsDataFiles.zip which containLAPD_Reporting_Districts-shp and LAReportingDistricts.prj.gda

This presentation looks at DMTI software. DMTI develops and markets web enabled spatial data and software products used in corporate and institutional information and marketing systems.

How to cite

When you use the datasets or maps, please also cite to the following paper introducing the whole of process from data collection, harmonization and visualization until releasing the data:

Rantanen, T., Tolvanen, H., Roose, M., Ylikoski, J. & Vesakoski, O. (2022) “Best practices for spatial language data harmonization, sharing and map creation - A case study of Uralic” PLoS ONE 17(6): e0269648. https://doi.org/10.1371/journal.pone.0269648.

Overview

The Geographical database of the Uralic languages consists of past and current distributions of the Uralic languages both as the original digital spatial datasets and as finalized maps. The database has been collected by the interdisciplinary BEDLAN (Biological Evolution and Diversification of LANguages) research team in collaboration with experts of Uralic languages. The work has been financed by the University of Turku (UTU–BGG), Kone Foundation (UraLex, AikaSyyni), the Academy of Finland (URKO), UiT – The Arctic University of Norway and the University of Oulu, as well as the Finno-Ugrian Society. The data have been compiled for the purposes of doing spatial linguistic and multidisciplinary research, and to visually present the state-of-the-art knowledge of the Uralic languages and their dialects. Geographic distributions are visualized as vector data primarily by using polygon objects (speaker areas or language areas), and in some rare cases, by using points. Based on the language distributions, coordinates for the languages and their dialects (point locations) have also been defined.

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GIS In Telecom Sector Market Size 2024-2028

The GIS in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

Geographic Information Systems (GIS) have gained significant traction In the telecom sector due to the increasing adoption of advanced technologies such as big data, sensors, drones, and LiDAR. The use of GIS enables telecom companies to effectively manage and analyze large volumes of digital data, including satellite and GPS information, to optimize infrastructure monitoring and antenna placement. In the context of smart cities, GIS plays a crucial role in enabling efficient communication between developers and end-users by providing real-time data on construction progress and infrastructure status. Moreover, the integration of LiDAR technology with drones offers enhanced capabilities for surveying and mapping telecom infrastructure, leading to improved accuracy and efficiency.
However, the implementation of GIS In the telecom sector also presents challenges, including data security concerns and the need for servers and computers to handle the large volumes of data generated by these technologies. In summary, the telecom sector's growing reliance on digital technologies such as GIS, big data, sensors, drones, and LiDAR is driving market growth, while the need for effective data management and security solutions presents challenges that must be addressed.

What will be the Size of the GIS In Telecom Sector Market During the Forecast Period?

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The Geographic Information System (GIS) market In the telecom sector is experiencing significant growth due to the increasing demand for electronic information and visual representation of data in various industries. This market encompasses a range of hardware and software solutions, including GNSS/GPS antennas, Lidar, GIS collectors, total stations, imaging sensors, and more. Major industries such as agriculture, oil & gas, architecture, and infrastructure monitoring are leveraging GIS technology for data analysis and decision-making. The adoption rate of GIS In the telecom sector is driven by the need for efficient data management and analysis, as well as the integration of real-time data from various sources.
Data formats and sources vary widely, from satellite and aerial imagery to ground-based sensors and IoT devices. The market is also witnessing innovation from startups and established players, leading to advancements in data processing capabilities and integration with other technologies like 5G networks and AI. Applications of GIS In the telecom sector include smart urban planning, smart utilities, and smart public works, among others.

How is this GIS In Telecom Sector Industry segmented and which is the largest segment?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  APAC

    China


  North America

    Canada
    US


  Europe

    UK
    Italy


  South America



  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. The telecom sector's Global GIS market encompasses software solutions for desktops, mobiles, cloud, and servers, along with developers' platforms. companies provide industry-specific GIS software, expanding the growth potential of this segment. Telecom companies heavily utilize intelligent maps generated by GIS for informed decisions on capacity planning and enhancements, such as improved service and next-generation networks. This drives significant growth In the software segment. Commercial entities offer open-source GIS software to counteract the threat of counterfeit products.
GIS technologies are integral to telecom network management, spatial data analysis, infrastructure planning, location-based services, network coverage mapping, data visualization, asset management, real-time network monitoring, design, wireless network mapping, integration, maintenance, optimization, and geospatial intelligence. Key applications include 5G network planning, network visualization, outage management, geolocation, mobile network optimization, and smart infrastructure planning. The GIS industry caters to major industries, including agriculture, oil & gas, architecture, engineering, construction, mining, utilities, retail, healthcare, government, and smart city planning. GIS solutions facilitate real-time data management, spatial information, and non-spatial information, offering enterprise solutions and transportation applications.

Get a glance at the market report of share of variou

Boundary of the UNESCO Biosphere Preserve on Niwot Ridge, Colorado. Not for any legal use, merely for display only. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

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Geospatial Analytics Market Size 2024-2028

The geospatial analytics market size is forecast to increase by USD 127.2 billion at a CAGR of 18.68% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of geospatial data analytics in sectors such as healthcare and insurance. This trend is driven by the abundance of data being generated through emerging methods like remote sensing, IoT, and drones. However, data privacy and security concerns remain a challenge, as geospatial data can reveal sensitive information.
Organizations must implement robust security measures to protect this valuable information. In the US and North America, the market is expected to grow steadily, driven by the region's advanced technological infrastructure and increasing focus on data-driven decision-making. Companies in this space should stay abreast of emerging trends and address concerns related to data security to remain competitive.

What will be the Size of the Geospatial Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for location intelligence in various industries, particularly Medium Scale Enterprises (MSEs). This market is driven by the integration of Artificial Intelligence (AI) and machine learning (ML), enabling advanced data analysis and prediction capabilities. The Internet of Things (IoT) is also fueling market growth, as real-time location data is collected and analyzed for various applications, including disaster risk reduction. Hexagon and Luciad are among the key players in this market, offering advanced geospatial analytics solutions. Big data analysis, digital globe imagery, and Pitney Bowes' location intelligence offerings are also contributing to market expansion.
The integration of AI, ML, and 5G technology is expected to further accelerate growth, with applications ranging from supply chain optimization to web-based GIS platforms built using JavaScript and HTML5.

How is this Geospatial Analytics Industry segmented and which is the largest segment?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Technology

  GPS
  GIS
  Remote sensing
  Others


End-user

  BFSI
  Government and utilities
  Telecom
  Manufacturing and automotive
  Others


Component

  Software
  Service


Type

  Surface & Field Analytics
  Network & Location Analytics
  Geovisualization
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Technology Insights

The GPS segment is estimated to witness significant growth during the forecast period. The market is driven by various sectors including Defense & Internal Security, Retail & Logistics, Energy & Utilities, Agriculture, Healthcare & Life Sciences, Infrastructure, and GIS. Among these, GPS, a satellite-based radio navigation system, was the largest segment in 2023. Operated by the US Space Force, GPS enables geolocation and time information transmission to receivers, facilitating georeferencing, positioning, navigation, and time and frequency control. This technology is widely used in industries such as logistics, transportation, and surveying, making it a significant contributor to the market's growth. The Energy & Utilities sector also leverages geospatial analytics for infrastructure planning, asset management, and maintenance, further fueling market expansion.

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The GPS segment was valued at USD 29.90 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American region dominates the market due to the region's early adoption of advanced technologies and the maturity of industries, particularly in healthcare and the industrial sector. The healthcare industry's need for high-level analytics, driven by the COVID-19 pandemic, is a significant factor fueling market growth. In the industrial sector, the abundance of successful technology implementations leads to a faster rate of adoption. Geospatial analytics plays a crucial role in various applications. These applications provide valuable insights for businesses and governments, enabling informed decision-making and improving operational efficien

Explore the spatial patterns of the Human Development Index (HDI) to identify regional pat- terns and causal factors in the data. The GeoInquiry activity is available here.Educational standards addressed:APHG: VI:B2 Analyze spatial patterns of social and economic development – GNI per capita. APHG: VI:B1 Explain social and economic measures of development – HDI, Gender Inequali- ty Index (GII), Total Fertility Rate (TRF).APHG: VI:B6 Social and economic measures of development — Changes in fertilityand mortalityThis map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

The United Nations Geospatial Data, or Geodata, is a worldwide geospatial dataset of the United Nations. The United Nations Geodata is provided to facilitate the preparation of cartographic materials in the United Nations includes geometry, attributes and labels to facilitate the adequate depiction and naming of geographic features for the preparation of maps in accordance with United Nations policies and practices. The geospatial dataset include polygons/areas of countries (BNDA_simplified). Please refer this page for more information.

Spatial data layers and attributes collected.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Replication data for the Senate roll-call voting example in Chapter 4 of Spatial Analysis for the Social Sciences.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

This activity uses Map Viewer and is designed for intermediate users. We recommend MapMaker when getting started with maps in the classroom - see this StoryMap for the same activity in MapMaker.ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.AP skills & objectives (CED)Skill 6.A: Identify the scales of analysis presented by maps, quantitative and geospatial data, images, and landscapes.PSO-2.C: Explain how population distribution and density affect society and the environment.PSO-2.F: Explain ways that geographers depict and analyze population composition.Learning outcomesStudents will be able to identify U.S. population data and certain spatial patterns. More activitiesAll Human Geography GeoInquiriesAll GeoInquiries